Created a tool that can detect the sentiment polarity (either positive or negative) of Book reviews written in Bengali Text.
Collected 1k book reviews from different online book shops as well as social media groups. Among these reviews 528 reviews are labelled as positve and 472 reviews are labelled as negative sentiment.
Extract Unigram, Bigram and Trigram features from the cleaned Text and use the TF-idf vectorizer as a feature extraction technique.
Employed different machine learning classifiers for the classification purpose. The used classifiers are Logistic Regression, Decision Tree, Multinomial Naive Bayes, Support Vector Machine and so on.
Evaluate the performance of the classification for every gram feature. Accuracy, Precision, Recall, F1-score, ROC curve and Precision-Recall curve used as evaluation metrics.
Finally, created a client facing API using Flask. App link
Created a tool that can categorizes the Bengali news headlines into six category (National, Politics, International, Sports, Amusement, IT) using deep recurrent neural network.
A dataset of 0.13 Million news headlines is created. Chrome web scrapper used for scraping the news headlines from different Bengali online news portals such as Dainik Jugantor, Dainik Ittefaq, Dainik Kaler Kontho and so on.
Word embeeding feature represtations technique is used for extracting the semantic meaning of the words.
A deep learning model has been built by using a bidirectional gated recurrent network.
Finally, the model performance is evaluated using various evaluation measures such as confusion matrix, accuracy , precision, recall and f1-score.
Created a tool that can identify the sentiment of a restaurant review written in Bengali Text. It classifies a review as positive or negative sentiment.
Collected 1.4k Bengali restaurant reviews from different social media groups of food or restaurant reviews. Among these reviews 630 reviews are labelled as positve and 790 reviews are labelled as negative sentiment.
Extract Unigram, Bigram and Trigram features from the cleaned Text and use the TF-idf vectorizer as a feature extraction technique.
Employed different machine learning classifiers for the classification purpose. The used classifiers are Logistic Regression, Decision Tree, Multinomial Naive Bayes, Support Vector Machine, Stochastic Gradient Descent and so on.
Evaluate the performance of the classification for every gram feature. Accuracy, Precision, Recall, F1-score, ROC curve and Precision-Recall curve used as evaluation metrics.
Finally, created a client facing API using Flask and deployed into cloud using Heroku. App Link
Developed a machine learning model that can classify the sentimental category (positive, negative and neutral) of a news comment written in Bangla Text.
For the implementation a publicly available dataset of 12k news comments have been used.
To create the system TF-idf feature extraction technique with n-gram features have been used.
Analysed the performance of different machine learning algorithms for n-gram feature by using various evaluation metrics such as accuracy, precision, recall and f1-score.
Created a tool that can categorizes the Bengali news articles into 12 diffferent categories (Art, Politics, International, Sports, Science, Economics, Crime, Accident, Education, Entertainment, Environment, Opinion) using Deep Learning.
A publicly available dataset of 0.1 Million news articles is used to develop the system. The dataset consist 12 different categories news articles.
Word embeeding feature represtations technique is used for extracting the semantic meaning of the words.
A deep learning model has been built by using a Convolutional Neural Network and Long Short Term Memory.
The model performance is evaluated using various evaluation measures such as confusion matrix, accuracy , precision, recall and f1-score.
Finally, developed a client facing API using flask and heroku.
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